CelebA_Classification_PyTorch_Github.ipynb

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    "colab": {
      "name": " CelebA_Classification_PyTorch_Github",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "KuxOtFxlNhJx",
        "colab_type": "code",
        "outputId": "9ca01eed-af45-4d6e-a92e-f47feb9d7725",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HfLdDRy3Ns18",
        "colab_type": "text"
      },
      "source": [
        "## Data Splitting and Pre-processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TbP1W2AyN_1v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img_zip_path = '/content/drive/My Drive/CelebA/Img/img_align_celeba.zip'\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QNzoSg7DONCf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import zipfile\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6DCAVj8fOQAs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "zip_ref = zipfile.ZipFile(img_zip_path, 'r')\n",
        "zip_ref.extractall()\n",
        "zip_ref.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d0Vi0UUgOaf7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labels_path = '/content/drive/My Drive/CelebA/Anno/list_attr_celeba.txt' "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Prm47ewtQoV6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labels_df = pd.read_csv(labels_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "labels_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>202599</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
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              "    <tr>\n",
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              "                                              202599\n",
              "0  5_o_Clock_Shadow Arched_Eyebrows Attractive Ba...\n",
              "1  000001.jpg -1  1  1 -1 -1 -1 -1 -1 -1 -1 -1  1...\n",
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              "3  000003.jpg -1 -1 -1 -1 -1 -1  1 -1 -1 -1  1 -1...\n",
              "4  000004.jpg -1 -1  1 -1 -1 -1 -1 -1 -1 -1 -1 -1..."
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          "execution_count": 9
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          "base_uri": "https://localhost:8080/",
          "height": 34
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      },
      "source": [
        "len(labels_df.columns)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
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          "metadata": {
            "tags": []
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          "execution_count": 10
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      },
      "source": [
        "(labels_df['202599'][1]).split()[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "'000001.jpg'"
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          "metadata": {
            "tags": []
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          "execution_count": 11
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len((labels_df['202599'][1]).split()[1:])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "40"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
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      "cell_type": "code",
      "metadata": {
        "id": "k2bFLPBcTSiM",
        "colab_type": "code",
        "outputId": "ed19d210-a163-47da-ec21-e20e401bbc83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        }
      },
      "source": [
        "len((labels_df['202599'][0]).split()), (labels_df['202599'][0]).split()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40,\n",
              " ['5_o_Clock_Shadow',\n",
              "  'Arched_Eyebrows',\n",
              "  'Attractive',\n",
              "  'Bags_Under_Eyes',\n",
              "  'Bald',\n",
              "  'Bangs',\n",
              "  'Big_Lips',\n",
              "  'Big_Nose',\n",
              "  'Black_Hair',\n",
              "  'Blond_Hair',\n",
              "  'Blurry',\n",
              "  'Brown_Hair',\n",
              "  'Bushy_Eyebrows',\n",
              "  'Chubby',\n",
              "  'Double_Chin',\n",
              "  'Eyeglasses',\n",
              "  'Goatee',\n",
              "  'Gray_Hair',\n",
              "  'Heavy_Makeup',\n",
              "  'High_Cheekbones',\n",
              "  'Male',\n",
              "  'Mouth_Slightly_Open',\n",
              "  'Mustache',\n",
              "  'Narrow_Eyes',\n",
              "  'No_Beard',\n",
              "  'Oval_Face',\n",
              "  'Pale_Skin',\n",
              "  'Pointy_Nose',\n",
              "  'Receding_Hairline',\n",
              "  'Rosy_Cheeks',\n",
              "  'Sideburns',\n",
              "  'Smiling',\n",
              "  'Straight_Hair',\n",
              "  'Wavy_Hair',\n",
              "  'Wearing_Earrings',\n",
              "  'Wearing_Hat',\n",
              "  'Wearing_Lipstick',\n",
              "  'Wearing_Necklace',\n",
              "  'Wearing_Necktie',\n",
              "  'Young'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
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        "colab": {
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          "height": 204
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      },
      "source": [
        "labels_df.tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "                                                   202599\n",
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        "label_dict = {}\n",
        "for i in range(1, len(labels_df)):\n",
        "    label_dict[labels_df['202599'][i].split()[0]] = [x for x in labels_df['202599'][i].split()[1:]]\n",
        "    \n"
      ],
      "execution_count": 0,
      "outputs": []
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        "colab": {
          "base_uri": "https://localhost:8080/",
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      },
      "source": [
        "len(label_dict)    "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "202599"
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      "source": [
        "label_df = pd.DataFrame(label_dict).T"
      ],
      "execution_count": 0,
      "outputs": []
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      "cell_type": "code",
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              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>000004.jpg</th>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>000005.jpg</th>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            0   1   2   3   4   5   6   7   ...  32  33  34  35  36  37  38 39\n",
              "000001.jpg  -1   1   1  -1  -1  -1  -1  -1  ...   1  -1   1  -1   1  -1  -1  1\n",
              "000002.jpg  -1  -1  -1   1  -1  -1  -1   1  ...  -1  -1  -1  -1  -1  -1  -1  1\n",
              "000003.jpg  -1  -1  -1  -1  -1  -1   1  -1  ...  -1   1  -1  -1  -1  -1  -1  1\n",
              "000004.jpg  -1  -1   1  -1  -1  -1  -1  -1  ...   1  -1   1  -1   1   1  -1  1\n",
              "000005.jpg  -1   1   1  -1  -1  -1   1  -1  ...  -1  -1  -1  -1   1  -1  -1  1\n",
              "\n",
              "[5 rows x 40 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "63NgFM-NVV0k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_df.columns = (labels_df['202599'][0]).split()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GvxZI1FnV3Me",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_df.replace(['-1'], ['0'], inplace = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SfC2KB_DV9SF",
        "colab_type": "code",
        "outputId": "1219b942-fff0-48f1-f562-4661fd36ebe7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "label_df.index\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['000001.jpg', '000002.jpg', '000003.jpg', '000004.jpg', '000005.jpg',\n",
              "       '000006.jpg', '000007.jpg', '000008.jpg', '000009.jpg', '000010.jpg',\n",
              "       ...\n",
              "       '202590.jpg', '202591.jpg', '202592.jpg', '202593.jpg', '202594.jpg',\n",
              "       '202595.jpg', '202596.jpg', '202597.jpg', '202598.jpg', '202599.jpg'],\n",
              "      dtype='object', length=202599)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zSiZNm4hWOlx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from PIL import Image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kWnhHWfIcuES",
        "colab_type": "code",
        "outputId": "53752d38-688c-40a9-e919-4ab33d840fbb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "Image.open('img_align_celeba/000001.jpg')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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rbOAl4/8+ujqRx6OfrPmilzOkXLZCnF8GnOdbWE0OQghc1zCMbDbrhcEcx3EcRxEUERNF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\nq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C7uO83A7aytzDPfCiXCg2ODBILAaQnmKJIghtzYWEqPDykqWt9YVsLyjsy96xcF1AVt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\nDgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=178x218 at 0x7EFE7BDF1F28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0tEp2fg_cy_r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from glob import glob"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ub9U1uXZeDEl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "files = glob('img_align_celeba/*.jpg')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hoMMpXvBeKsT",
        "colab_type": "code",
        "outputId": "3fa5aeb0-592f-425e-ccab-675f214f62fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(files)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "202599"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X3X6NerheL1_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "shuffle = np.random.permutation(len(files))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uDhP24f-eUUz",
        "colab_type": "code",
        "outputId": "7b9a987e-7e20-49c4-b281-8998b8251589",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(shuffle)*0.3"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60779.7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p3A87pPDeVgF",
        "colab_type": "code",
        "outputId": "33bbddf7-a8d6-404d-e506-42bde4016a1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "max(shuffle)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "202598"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IUbPdO8ieqyT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in ['train', 'valid']:\n",
        "    os.mkdir(os.path.join('/tmp/', i))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_BJwotezpzAO",
        "colab_type": "code",
        "outputId": "51590d24-82ac-414f-af67-b25d14aec5fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!ls /tmp/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "drivefs_ipc.0  drivefs_ipc.0_shell  tmprsfzvhy3  train\tvalid\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ac09TOVCp0rW",
        "colab_type": "code",
        "outputId": "f8db561c-b083-4b98-a5b1-13fb06dce35d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "files[10].split('/')[-1]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'006433.jpg'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-yas6hE4p4Rn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# label_df.find(files[10].split('/')[-1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3X6EoGGRqVCc",
        "colab_type": "code",
        "outputId": "8c351db8-c01f-4b83-e460-a492cbec4c73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "((np.array(label_df[label_df.index==files[10].split('/')[-1]]))).shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 40)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FDBkjlLQi2My",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tqdm import tqdm"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fH0BvMIOr0jT",
        "colab_type": "code",
        "outputId": "a4613fae-0c50-4f5e-b0ce-d992834f7fc5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "valid_dict = {}\n",
        "valid_file_names = []\n",
        "for i in tqdm(shuffle[:6000]):\n",
        "    file_name = files[i].split('/')[-1]\n",
        "    labels = np.array(label_df[label_df.index==file_name])\n",
        "    valid_dict[file_name] = labels\n",
        "    valid_file_names.append(file_name)\n",
        "    os.rename(files[i], os.path.join('/tmp/', '/tmp/valid', file_name))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 6000/6000 [01:08<00:00, 87.28it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D-9lUHGSswRa",
        "colab_type": "code",
        "outputId": "5d85fa1c-9f84-4d99-f1c2-bb887a821c8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(glob('/tmp/valid/*.jpg'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YiU9sA4ZxnKJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Image.open('/tmp/valid/000004.jpg')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XdvxIjYzxtBH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# valid_dict.values()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yso9Nhrcxv8Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "valid_df = pd.DataFrame(valid_dict.values())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dspEU2TVxzGU",
        "colab_type": "code",
        "outputId": "b263ac8d-e439-4770-83cf-2eae25e18bd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "valid_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th>0</th>\n",
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              "      <th>1</th>\n",
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              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>[[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,...</td>\n",
              "    </tr>\n",
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              "      <td>[[0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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            "text/plain": [
              "                                                   0\n",
              "0  [[1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,...\n",
              "1  [[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,...\n",
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          },
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            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nqRdIOSby9_S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "valid_df.index = valid_file_names"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ajO-1PfzDAJ",
        "colab_type": "code",
        "outputId": "02d0d081-8aa6-448d-ae90-cc9cd3f906d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "valid_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>064465.jpg</th>\n",
              "      <td>[[1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>013351.jpg</th>\n",
              "      <td>[[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>000263.jpg</th>\n",
              "      <td>[[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152775.jpg</th>\n",
              "      <td>[[0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184761.jpg</th>\n",
              "      <td>[[0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                            0\n",
              "064465.jpg  [[1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,...\n",
              "013351.jpg  [[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,...\n",
              "000263.jpg  [[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,...\n",
              "152775.jpg  [[0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,...\n",
              "184761.jpg  [[0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mX37QruszEMF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "valid_df.columns = ['labels']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xD_6ZiiizM8h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "                    # (valid_df.labels[valid_df.index == '130656.jpg'].values.tolist()[0]).astype(float), ((valid_df.labels[valid_df.index == '130656.jpg'].values.tolist()[0]).astype(float)).shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xGeCp_hRz7dA",
        "colab_type": "code",
        "outputId": "5d435cbd-07cd-496b-916f-afabba84d849",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_dict = {}\n",
        "train_file_names = []\n",
        "for i in tqdm(shuffle[6000:18000 ]):\n",
        "    file_name = files[i].split('/')[-1]\n",
        "    labels = np.array(label_df[label_df.index==file_name])\n",
        "    train_dict[file_name] = labels\n",
        "    train_file_names.append(file_name)\n",
        "    os.rename(files[i], os.path.join('/tmp/', '/tmp/train', file_name))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 12000/12000 [02:17<00:00, 87.05it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2qIBO9343hBc",
        "colab_type": "code",
        "outputId": "90bbf618-a3dc-47e1-ec0e-98f209ec0072",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(train_dict)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBDEaBsZISmZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = pd.DataFrame(train_dict.values())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPBzZBRKIe5v",
        "colab_type": "code",
        "outputId": "64e516a8-275d-450f-df19-72c523314f6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[[0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[[0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[[0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,...</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
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            "text/plain": [
              "                                                   0\n",
              "0  [[0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1,...\n",
              "1  [[0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,...\n",
              "2  [[0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,...\n",
              "3  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,...\n",
              "4  [[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gHH2MAcRIgPc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df.index = train_file_names"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WJVlRWDmImNS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df.columns = ['labels']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C32Ci-34Fppk",
        "colab_type": "text"
      },
      "source": [
        "# Generating Batches and Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uH3EL725rYm6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torchvision"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LXML6_A0L3Pu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from glob import glob\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "from torchvision import transforms\n",
        "from torchvision import models\n",
        "import torch\n",
        "from torch.autograd import Variable\n",
        "import torch.nn as nn\n",
        "from torch.optim import lr_scheduler\n",
        "from torch import optim\n",
        "from torchvision.utils import make_grid\n",
        "import time\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IGy_I0ovL7RE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torch.utils.data import Dataset"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7htP_GRYIou5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class MultiClassCelebA(Dataset):\n",
        "    \n",
        "    def __init__(self, dataframe, folder_dir, transform = None):\n",
        "        \n",
        "        self.dataframe = dataframe\n",
        "        self.folder_dir = folder_dir\n",
        "        self.transform = transform\n",
        "        self.file_names = dataframe.index\n",
        "        self.labels = dataframe.labels.values.tolist()\n",
        "        \n",
        "        \n",
        "    def __len__(self):\n",
        "        return len(self.dataframe)\n",
        "    \n",
        "    \n",
        "    def __getitem__(self, index):\n",
        "        \n",
        "        image = Image.open(os.path.join(self.folder_dir, self.file_names[index]))\n",
        "        label = self.labels[index][0]\n",
        "        sample = {'image': image, 'label': label.astype(float)}\n",
        "        if self.transform:\n",
        "            image = self.transform(sample['image'])\n",
        "            sample = {'image': image, 'label': label.astype(float)}\n",
        "        \n",
        "        return sample\n",
        "        \n",
        "        \n",
        "        "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "320blHDsFkob",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tfms = transforms.Compose([transforms.Resize((256, 256)),\n",
        "                           transforms.ToTensor()])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5oOC9jzKPx--",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_dl = MultiClassCelebA(train_df, '/tmp/train/', transform = tfms)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ZAefqQTQgnt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# plt.imshow((train_dl[1]['image']).reshape(3, 256, 256))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xb-Ko1FUQ77f",
        "colab_type": "code",
        "outputId": "7e5c4a8b-4e02-4f61-8696-1e5c2bb6421d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "plt.imshow(torchvision.utils.make_grid(train_dl[1]['image']).permute(1, 2, 0))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7efe2d774198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Md13n4jql9THya6UYfvliZma3Zq6WAsWcE5s4TsScWbeurLn46bkcJ2smx0hK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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ak-SsNTSdiF",
        "colab_type": "code",
        "outputId": "a6bd142a-f5ad-4ae8-9d24-e504dd6016b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "(train_dl[1]['label'].astype(float))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 1., 1., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 1., 1., 0., 1., 0., 1., 1., 0., 0., 0., 0., 0., 0., 1., 0., 1.,\n",
              "       1., 0., 1., 0., 0., 1.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5l8B1AJBSuSS",
        "colab_type": "code",
        "outputId": "147ee131-b95a-4a99-9c0f-7e4b7de0dcc4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        }
      },
      "source": [
        "label_df.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['5_o_Clock_Shadow', 'Arched_Eyebrows', 'Attractive', 'Bags_Under_Eyes',\n",
              "       'Bald', 'Bangs', 'Big_Lips', 'Big_Nose', 'Black_Hair', 'Blond_Hair',\n",
              "       'Blurry', 'Brown_Hair', 'Bushy_Eyebrows', 'Chubby', 'Double_Chin',\n",
              "       'Eyeglasses', 'Goatee', 'Gray_Hair', 'Heavy_Makeup', 'High_Cheekbones',\n",
              "       'Male', 'Mouth_Slightly_Open', 'Mustache', 'Narrow_Eyes', 'No_Beard',\n",
              "       'Oval_Face', 'Pale_Skin', 'Pointy_Nose', 'Receding_Hairline',\n",
              "       'Rosy_Cheeks', 'Sideburns', 'Smiling', 'Straight_Hair', 'Wavy_Hair',\n",
              "       'Wearing_Earrings', 'Wearing_Hat', 'Wearing_Lipstick',\n",
              "       'Wearing_Necklace', 'Wearing_Necktie', 'Young'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ug0_1LgVTAn2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "valid_dl = MultiClassCelebA(valid_df, '/tmp/valid/', transform = tfms)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7RpnZ8nkUYOa",
        "colab_type": "code",
        "outputId": "bb9ba11c-eef5-484c-bd95-f9d3f3b29a15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "plt.imshow(torchvision.utils.make_grid(valid_dl[1]['image']).permute(1,2,0))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7efe2d2b6b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qchZh3Hc9I6/TaCt0ZZSkyVOKp0+fcnV1zeXVTbHNbckDKYCxMBdlTC8WC0KI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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qYx0PctxU38b",
        "colab_type": "code",
        "outputId": "4c3c1ccd-8b5b-4db5-ea01-6c7038995c11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "valid_dl[1]['label']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 1., 1., 0., 1., 0., 0., 1., 1., 0., 0., 0., 1., 0., 1., 0., 1.,\n",
              "       0., 0., 1., 0., 0., 1.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C6pjWLkPVImv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.nn.functional as F"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oC_7CQyUU7h4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class MultiClassifier(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(MultiClassifier, self).__init__()\n",
        "        self.ConvLayer1 = nn.Sequential(\n",
        "            nn.Conv2d(3, 64, 3), # 3, 256, 256\n",
        "            nn.MaxPool2d(2), # op: 16, 127, 127\n",
        "            nn.ReLU(), # op: 64, 127, 127\n",
        "        )\n",
        "        self.ConvLayer2 = nn.Sequential(\n",
        "            nn.Conv2d(64, 128, 3), # 64, 127, 127   \n",
        "            nn.MaxPool2d(2), #op: 128, 63, 63\n",
        "            nn.ReLU() # op: 128, 63, 63\n",
        "        )\n",
        "        self.ConvLayer3 = nn.Sequential(\n",
        "            nn.Conv2d(128, 256, 3), # 128, 63, 63\n",
        "            nn.MaxPool2d(2), #op: 256, 30, 30\n",
        "            nn.ReLU() #op: 256, 30, 30\n",
        "        )\n",
        "        self.ConvLayer4 = nn.Sequential(\n",
        "            nn.Conv2d(256, 512, 3), # 256, 30, 30\n",
        "            nn.MaxPool2d(2), #op: 512, 14, 14\n",
        "            nn.ReLU(), #op: 512, 14, 14\n",
        "            nn.Dropout(0.2)\n",
        "        )\n",
        "        self.Linear1 = nn.Linear(512 * 14 * 14, 1024)\n",
        "        self.Linear2 = nn.Linear(1024, 256)\n",
        "        self.Linear3 = nn.Linear(256, 40)\n",
        "        \n",
        "        \n",
        "    def forward(self, x):\n",
        "        x = self.ConvLayer1(x)\n",
        "        x = self.ConvLayer2(x)\n",
        "        x = self.ConvLayer3(x)\n",
        "        x = self.ConvLayer4(x)\n",
        "        x = x.view(x.size(0), -1)\n",
        "        x = self.Linear1(x)\n",
        "        x = self.Linear2(x)\n",
        "        x = self.Linear3(x)\n",
        "        return F.sigmoid(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eIqeGgyEVKTF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def check_cuda():\n",
        "    _cuda = False\n",
        "    if torch.cuda.is_available():\n",
        "        _cuda = True\n",
        "    return _cuda"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-r2QrdspVMWK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "is_cuda = check_cuda()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZCSiMgDEVO2I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = MultiClassifier()\n",
        "if is_cuda:\n",
        "    model.cuda()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HyMUQH_SVQvV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_dataloader = torch.utils.data.DataLoader(train_dl, shuffle = True, batch_size = 64, num_workers = 3)\n",
        "valid_dataloader = torch.utils.data.DataLoader(valid_dl, shuffle = True, batch_size = 64, num_workers = 3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B_zLsj8qdeuo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def pred_acc(original, predicted):\n",
        "    return torch.round(predicted).eq(original).sum().numpy()/len(original)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JNVKy1rUAHN6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "criterion = nn.BCELoss()\n",
        "optimizer = optim.SGD(model.parameters(), lr = 0.001, momentum = 0.9)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7gL2huUIqy3U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pprint import pprint"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zQq0u4ABpW1E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def fit_model(epochs, model, dataloader, phase = 'training', volatile = False):\n",
        "    \n",
        "    pprint(\"Epoch: {}\".format(epochs))\n",
        "\n",
        "    if phase == 'training':\n",
        "        model.train()\n",
        "        \n",
        "    if phase == 'validataion':\n",
        "        model.eval()\n",
        "        volatile = True\n",
        "        \n",
        "    running_loss = []\n",
        "    running_acc = []\n",
        "    b = 0\n",
        "    for i, data in enumerate(dataloader):\n",
        "        \n",
        "\n",
        "        inputs, target = data['image'].cuda(), data['label'].float().cuda()\n",
        "        \n",
        "        inputs, target = Variable(inputs), Variable(target)\n",
        "        \n",
        "        if phase == 'training':\n",
        "            optimizer.zero_grad()\n",
        "            \n",
        "        ops = model(inputs)\n",
        "        \n",
        "        acc_ = []\n",
        "        for i, d in enumerate(ops, 0):\n",
        "           \n",
        "            acc = pred_acc(torch.Tensor.cpu(target[i]), torch.Tensor.cpu(d))\n",
        "            acc_.append(acc)\n",
        "\n",
        "        loss = criterion(ops, target)\n",
        "                \n",
        "        running_loss.append(loss.item())\n",
        "        running_acc.append(np.asarray(acc_).mean())\n",
        "        b += 1\n",
        "       \n",
        "        if phase == 'training':\n",
        "            \n",
        "            loss.backward()\n",
        "        \n",
        "            optimizer.step()\n",
        "            \n",
        "    total_batch_loss = np.asarray(running_loss).mean()\n",
        "    total_batch_acc = np.asarray(running_acc).mean()\n",
        "    \n",
        "\n",
        "    pprint(\"{} loss is {} \".format(phase,total_batch_loss))\n",
        "    pprint(\"{} accuracy is {} \".format(phase, total_batch_acc))\n",
        "    \n",
        "    return total_batch_loss, total_batch_acc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ut5r3gaXr3ny",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "trn_losses = []; trn_acc = []\n",
        "val_losses = []; val_acc = []\n",
        "for i in tqdm(range(1, 20)):\n",
        "    trn_l, trn_a = fit_model(i, model, train_dataloader)\n",
        "    val_l, val_a = fit_model(i, model, valid_dataloader, phase = 'validation')\n",
        "    trn_losses.append(trn_l); trn_acc.append(trn_a)\n",
        "    val_losses.append(val_l); val_acc.append(val_a)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dLwrB2AsVI21",
        "colab_type": "code",
        "outputId": "37e2c9bc-17a4-48cf-d73c-a63253a5ebad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        }
      },
      "source": [
        "torch.save(model, \"Model_40_total_EPCHS\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type MultiClassifier. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type Sequential. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type Conv2d. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type MaxPool2d. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type ReLU. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type Dropout. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type Linear. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
          ],
          "name": "stderr"
        }
      ]
    }
  ]
}